#ifndef LLAMA_H
#define LLAMA_H

#include "ggml.h"
#include "ModelMeta.h" // for save_llama_model_file

#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
#else
#define LLAMA_MAX_DEVICES 1
#endif // GGML_USE_CUBLAS
#include <stddef.h>
#include <stdint.h>
#include <stdio.h>
#include <stdbool.h>

#ifdef LLAMA_SHARED
#    if defined(_WIN32) && !defined(__MINGW32__)
#        ifdef LLAMA_BUILD
#            define LLAMA_API __declspec(dllexport)
#        else
#            define LLAMA_API __declspec(dllimport)
#        endif
#    else
#        define LLAMA_API __attribute__ ((visibility ("default")))
#    endif
#else
#    define LLAMA_API
#endif

#ifdef __GNUC__
#    define DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
#elif defined(_MSC_VER)
#    define DEPRECATED(func, hint) __declspec(deprecated(hint)) func
#else
#    define DEPRECATED(func, hint) func
#endif

#define LLAMA_DEFAULT_SEED 0xFFFFFFFF

#define LLAMA_MAX_RNG_STATE (64*1024)

#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'

#define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 2

#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
#define LLAMA_SUPPORTS_GPU_OFFLOAD
#endif

#ifdef __cplusplus
extern "C" {
#endif

//
// C interface
//
// TODO: show sample usage
//

struct llama_model;
struct llama_context;

typedef int32_t llama_pos;
typedef int32_t llama_token;
typedef int32_t llama_seq_id;

enum llama_vocab_type {
    LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
    LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
};

enum llama_token_type {
    LLAMA_TOKEN_TYPE_UNDEFINED    = 0,
    LLAMA_TOKEN_TYPE_NORMAL       = 1,
    LLAMA_TOKEN_TYPE_UNKNOWN      = 2,
    LLAMA_TOKEN_TYPE_CONTROL      = 3,
    LLAMA_TOKEN_TYPE_USER_DEFINED = 4,
    LLAMA_TOKEN_TYPE_UNUSED       = 5,
    LLAMA_TOKEN_TYPE_BYTE         = 6,
};

// model file types
enum llama_ftype {
    LLAMA_FTYPE_ALL_F32              = 0,
    LLAMA_FTYPE_MOSTLY_F16           = 1,  // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q4_0          = 2,  // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q4_1          = 3,  // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4,  // tok_embeddings.weight and output.weight are F16
    // LLAMA_FTYPE_MOSTLY_Q4_2       = 5,  // support has been removed
    // LLAMA_FTYPE_MOSTLY_Q4_3       = 6,  // support has been removed
    LLAMA_FTYPE_MOSTLY_Q8_0          = 7,  // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q5_0          = 8,  // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q5_1          = 9,  // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q2_K          = 10, // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q3_K_S        = 11, // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q3_K_M        = 12, // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q3_K_L        = 13, // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q4_K_S        = 14, // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q4_K_M        = 15, // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q5_K_S        = 16, // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q5_K_M        = 17, // except 1d tensors
    LLAMA_FTYPE_MOSTLY_Q6_K          = 18, // except 1d tensors

    LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};

enum llama_rope_scaling_type {
    LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
    LLAMA_ROPE_SCALING_NONE        = 0,
    LLAMA_ROPE_SCALING_LINEAR      = 1,
    LLAMA_ROPE_SCALING_YARN        = 2,
    LLAMA_ROPE_SCALING_MAX_VALUE   = LLAMA_ROPE_SCALING_YARN,
};

typedef struct llama_token_data {
    llama_token id; // token id
    float logit;    // log-odds of the token
    float p;        // probability of the token
} llama_token_data;

typedef struct llama_token_data_array {
    llama_token_data * data;
    size_t size;
    bool sorted;
} llama_token_data_array;

typedef void (*llama_progress_callback)(float progress, void *ctx);

// Input data for llama_decode
// A llama_batch object can contain input about one or many sequences
// The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
//
// - token  : the token ids of the input (used when embd is NULL)
// - embd   : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
// - pos    : the positions of the respective token in the sequence
// - seq_id : the sequence to which the respective token belongs
// - logits : if zero, the logits for the respective token will not be output
//
typedef struct llama_batch {
    int32_t n_tokens;

    llama_token  *  token;
    float        *  embd;
    llama_pos    *  pos;
    int32_t      *  n_seq_id;
    llama_seq_id ** seq_id;
    int8_t       *  logits;

    // NOTE: helpers for smooth API transition - can be deprecated in the future
    //       for future-proof code, use the above fields instead and ignore everything below
    //
    // pos[i] = all_pos_0 + i*all_pos_1
    //
    llama_pos    all_pos_0;  // used if pos == NULL
    llama_pos    all_pos_1;  // used if pos == NULL
    llama_seq_id all_seq_id; // used if seq_id == NULL
} llama_batch;

struct llama_model_params {
    int32_t n_gpu_layers; // number of layers to store in VRAM
    int32_t main_gpu;     // the GPU that is used for scratch and small tensors
    const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)

    // called with a progress value between 0 and 1, pass NULL to disable
    llama_progress_callback progress_callback;
    // context pointer passed to the progress callback
    void * progress_callback_user_data;

    // Keep the booleans together to avoid misalignment during copy-by-value.
    bool vocab_only; // only load the vocabulary, no weights
    bool use_mmap;   // use mmap if possible
    bool use_mlock;  // force system to keep model in RAM
};

struct llama_context_params {
    uint32_t seed;              // RNG seed, -1 for random
    uint32_t n_ctx;             // text context, 0 = from model
    uint32_t n_batch;           // prompt processing maximum batch size
    uint32_t n_threads;         // number of threads to use for generation
    uint32_t n_threads_batch;   // number of threads to use for batch processing
    int8_t   rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`

    // ref: https://github.com/ggerganov/llama.cpp/pull/2054
    float    rope_freq_base;   // RoPE base frequency, 0 = from model
    float    rope_freq_scale;  // RoPE frequency scaling factor, 0 = from model
    float    yarn_ext_factor;  // YaRN extrapolation mix factor, NaN = from model
    float    yarn_attn_factor; // YaRN magnitude scaling factor
    float    yarn_beta_fast;   // YaRN low correction dim
    float    yarn_beta_slow;   // YaRN high correction dim
    uint32_t yarn_orig_ctx;    // YaRN original context size

    // Keep the booleans together to avoid misalignment during copy-by-value.
    bool mul_mat_q;  // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
    bool f16_kv;     // use fp16 for KV cache, fp32 otherwise
    bool logits_all; // the llama_eval() call computes all logits, not just the last one
    bool embedding;  // embedding mode only
};

// model quantization parameters
typedef struct llama_model_quantize_params {
    int nthread;                 // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
    enum llama_ftype ftype;      // quantize to this llama_ftype
    bool allow_requantize;       // allow quantizing non-f32/f16 tensors
    bool quantize_output_tensor; // quantize output.weight
    bool only_copy;              // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
    bool pure;                   // disable k-quant mixtures and quantize all tensors to the same type
} llama_model_quantize_params;

// grammar types
struct llama_grammar;

// grammar element type
enum llama_gretype {
    // end of rule definition
    LLAMA_GRETYPE_END            = 0,

    // start of alternate definition for rule
    LLAMA_GRETYPE_ALT            = 1,

    // non-terminal element: reference to rule
    LLAMA_GRETYPE_RULE_REF       = 2,

    // terminal element: character (code point)
    LLAMA_GRETYPE_CHAR           = 3,

    // inverse char(s) ([^a], [^a-b] [^abc])
    LLAMA_GRETYPE_CHAR_NOT       = 4,

    // modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
    // be an inclusive range ([a-z])
    LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,

    // modifies a preceding LLAMA_GRETYPE_CHAR or
    // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
    LLAMA_GRETYPE_CHAR_ALT       = 6,
};

typedef struct llama_grammar_element {
    enum llama_gretype type;
    uint32_t           value; // Unicode code point or rule ID
} llama_grammar_element;

// performance timing information
struct llama_timings {
    double t_start_ms;
    double t_end_ms;
    double t_load_ms;
    double t_sample_ms;
    double t_p_eval_ms;
    double t_eval_ms;

    int32_t n_sample;
    int32_t n_p_eval;
    int32_t n_eval;
};

// Helpers for getting default parameters
LLAMA_API struct llama_model_params llama_model_default_params(void);
LLAMA_API struct llama_context_params llama_context_default_params(void);
LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);

// Initialize the llama + ggml backend
// If numa is true, use NUMA optimizations
// Call once at the start of the program
LLAMA_API void llama_backend_init(bool numa);

// Call once at the end of the program - currently only used for MPI
LLAMA_API void llama_backend_free(void);

LLAMA_API struct llama_model * llama_load_model_from_file(
        const char * path_model,
        struct llama_model_params     params);

LLAMA_API void llama_free_model(struct llama_model * model);

LLAMA_API struct llama_context * llama_new_context_with_model(
        struct llama_model * model,
        struct llama_context_params   params);

// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);

LLAMA_API int64_t llama_time_us(void);

LLAMA_API int  llama_max_devices    (void);
LLAMA_API bool llama_mmap_supported (void);
LLAMA_API bool llama_mlock_supported(void);

LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);

LLAMA_API int llama_n_ctx      (const struct llama_context * ctx);

LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);

LLAMA_API int llama_n_vocab    (const struct llama_model * model);
LLAMA_API int llama_n_ctx_train(const struct llama_model * model);
LLAMA_API int llama_n_embd     (const struct llama_model * model);

// Get the model's RoPE frequency scaling factor
LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);

// Functions to access the model's GGUF metadata scalar values
// - The functions return the length of the string on success, or -1 on failure
// - The output string is always null-terminated and cleared on failure
// - GGUF array values are not supported by these functions

// Get metadata value as a string by key name
LLAMA_API int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);

// Get the number of metadata key/value pairs
LLAMA_API int llama_model_meta_count(const struct llama_model * model);

// Get metadata key name by index
LLAMA_API int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);

// Get metadata value as a string by index
LLAMA_API int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);

// Get a string describing the model type
LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);

// Returns the total size of all the tensors in the model in bytes
LLAMA_API uint64_t llama_model_size(const struct llama_model * model);

// Returns the total number of parameters in the model
LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);

// Get a llama model tensor
LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);

// Returns 0 on success
LLAMA_API int llama_model_quantize(
        const char * fname_inp,
        const char * fname_out,
        const llama_model_quantize_params * params);

// Apply a LoRA adapter to a loaded model
// path_base_model is the path to a higher quality model to use as a base for
// the layers modified by the adapter. Can be NULL to use the current loaded model.
// The model needs to be reloaded before applying a new adapter, otherwise the adapter
// will be applied on top of the previous one
// Returns 0 on success
LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
        struct llama_context * ctx,
        const char * path_lora,
        float   scale,
        const char * path_base_model,
                             int   n_threads),
                     "use llama_model_apply_lora_from_file instead");

LLAMA_API int llama_model_apply_lora_from_file(
        const struct llama_model * model,
        const char * path_lora,
        float   scale,
        const char * path_base_model,
        int   n_threads);

//
// KV cache
//

// Returns the number of tokens in the KV cache
LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx),
                     "avoid using this, it will be removed in the future, instead - count the tokens in user code");

// Clear the KV cache
LLAMA_API void llama_kv_cache_clear(
        struct llama_context * ctx);

// Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
// seq_id < 0 : match any sequence
// p0 < 0     : [0,  p1]
// p1 < 0     : [p0, inf)
LLAMA_API void llama_kv_cache_seq_rm(
        struct llama_context * ctx,
        llama_seq_id   seq_id,
        llama_pos   p0,
        llama_pos   p1);

// Copy all tokens that belong to the specified sequence to another sequence
// Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
// p0 < 0 : [0,  p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_cp(
        struct llama_context * ctx,
        llama_seq_id   seq_id_src,
        llama_seq_id   seq_id_dst,
        llama_pos   p0,
        llama_pos   p1);

// Removes all tokens that do not belong to the specified sequence
LLAMA_API void llama_kv_cache_seq_keep(
        struct llama_context * ctx,
        llama_seq_id   seq_id);

// Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
// If the KV cache is RoPEd, the KV data is updated accordingly
// p0 < 0 : [0,  p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_shift(
        struct llama_context * ctx,
        llama_seq_id   seq_id,
        llama_pos   p0,
        llama_pos   p1,
        llama_pos   delta);

//
// State / sessions
//

// Returns the maximum size in bytes of the state (rng, logits, embedding
// and kv_cache) - will often be smaller after compacting tokens
LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);

// Copies the state to the specified destination address.
// Destination needs to have allocated enough memory.
// Returns the number of bytes copied
LLAMA_API size_t llama_copy_state_data(
        struct llama_context * ctx,
        uint8_t * dst);

// Set the state reading from the specified address
// Returns the number of bytes read
LLAMA_API size_t llama_set_state_data(
        struct llama_context * ctx,
        uint8_t * src);

// Save/load session file
LLAMA_API bool llama_load_session_file(
        struct llama_context * ctx,
        const char * path_session,
        llama_token * tokens_out,
        size_t   n_token_capacity,
        size_t * n_token_count_out);

LLAMA_API bool llama_save_session_file(
        struct llama_context * ctx,
        const char * path_session,
        const llama_token * tokens,
        size_t   n_token_count);

//
// Decoding
//

// Run the llama inference to obtain the logits and probabilities for the next token(s).
// tokens + n_tokens is the provided batch of new tokens to process
// n_past is the number of tokens to use from previous eval calls
// Returns 0 on success
// DEPRECATED: use llama_decode() instead
LLAMA_API DEPRECATED(int llama_eval(
        struct llama_context * ctx,
        llama_token * tokens,
        int32_t   n_tokens,
                             int   n_past),
                     "use llama_decode() instead");

// Same as llama_eval, but use float matrix input directly.
// DEPRECATED: use llama_decode() instead
LLAMA_API DEPRECATED(int llama_eval_embd(
        struct llama_context * ctx,
        float * embd,
        int32_t   n_tokens,
                             int   n_past),
                     "use llama_decode() instead");

// Return batch for single sequence of tokens starting at pos_0
//
// NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
//
LLAMA_API struct llama_batch llama_batch_get_one(
        llama_token * tokens,
        int32_t   n_tokens,
        llama_pos   pos_0,
        llama_seq_id   seq_id);

// Allocates a batch of tokens on the heap that can hold a maximum of n_tokens
// Each token can be assigned up to n_seq_max sequence ids
// The batch has to be freed with llama_batch_free()
// If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
// Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
// The rest of the llama_batch members are allocated with size n_tokens
// All members are left uninitialized
LLAMA_API struct llama_batch llama_batch_init(
        int32_t n_tokens,
        int32_t embd,
        int32_t n_seq_max);

// Frees a batch of tokens allocated with llama_batch_init()
LLAMA_API void llama_batch_free(struct llama_batch batch);

// Positive return values does not mean a fatal error, but rather a warning.
//   0 - success
//   1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
// < 0 - error
LLAMA_API int llama_decode(
        struct llama_context * ctx,
        struct llama_batch   batch);

// Set the number of threads used for decoding
// n_threads is the number of threads used for generation (single token)
// n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);

// Token logits obtained from the last call to llama_eval()
// The logits for the last token are stored in the last row
// Logits for which llama_batch.logits[i] == 0 are undefined
// Rows: n_tokens provided with llama_batch
// Cols: n_vocab
LLAMA_API float * llama_get_logits(struct llama_context * ctx);

// Logits for the ith token. Equivalent to:
// llama_get_logits(ctx) + i*n_vocab
LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);

// Get the embeddings for the input
// shape: [n_embd] (1-dimensional)
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);

//
// Vocab
//

LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);

LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);

LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);

// Special tokens
LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line

// Returns -1 if unknown, 1 for true or 0 for false.
LLAMA_API int         llama_add_bos_token(const struct llama_model * model);

// Returns -1 if unknown, 1 for true or 0 for false.
LLAMA_API int         llama_add_eos_token(const struct llama_model * model);

// codellama infill tokens
LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
LLAMA_API llama_token llama_token_eot   (const struct llama_model * model); // End of infill middle

//
// Tokenization
//

/// @details Convert the provided text into tokens.
/// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
/// @return Returns the number of tokens on success, no more than n_max_tokens
/// @return Returns a negative number on failure - the number of tokens that would have been returned
/// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
///                Does not insert a leading space.
LLAMA_API int llama_tokenize(
        const struct llama_model * model,
        const char * text,
        int   text_len,
        llama_token * tokens,
        int   n_max_tokens,
        bool   add_bos,
        bool   special);

// Token Id -> Piece.
// Uses the vocabulary in the provided context.
// Does not write null terminator to the buffer.
// User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
LLAMA_API int llama_token_to_piece(
        const struct llama_model * model,
        llama_token   token,
        char * buf,
        int    length);

//
// Grammar
//

LLAMA_API struct llama_grammar * llama_grammar_init(
        const llama_grammar_element ** rules,
        size_t    n_rules,
        size_t    start_rule_index);

LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);

LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);

//
// Sampling functions
//

// Sets the current rng seed.
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);

/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
LLAMA_API void llama_sample_repetition_penalties(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        const llama_token * last_tokens,
        size_t   penalty_last_n,
        float   penalty_repeat,
        float   penalty_freq,
        float   penalty_present);

/// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
/// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
/// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
LLAMA_API void llama_sample_classifier_free_guidance(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        struct llama_context * guidance_ctx,
        float   scale);

/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
LLAMA_API void llama_sample_softmax(
        struct llama_context * ctx,
        llama_token_data_array * candidates);

/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
LLAMA_API void llama_sample_top_k(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        int   k,
        size_t   min_keep);

/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
LLAMA_API void llama_sample_top_p(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        float   p,
        size_t   min_keep);

/// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
LLAMA_API void llama_sample_min_p(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        float   p,
        size_t   min_keep);

/// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
LLAMA_API void llama_sample_tail_free(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        float   z,
        size_t   min_keep);

/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
LLAMA_API void llama_sample_typical(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        float   p,
        size_t   min_keep);

LLAMA_API void llama_sample_temp(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        float   temp);

LLAMA_API DEPRECATED(void llama_sample_temperature(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
                             float   temp),
                     "use llama_sample_temp instead");

/// @details Apply constraints from grammar
LLAMA_API void llama_sample_grammar(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        const struct llama_grammar * grammar);

/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
/// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
/// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
LLAMA_API llama_token llama_sample_token_mirostat(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        float   tau,
        float   eta,
        int   m,
        float * mu);

/// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
/// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
LLAMA_API llama_token llama_sample_token_mirostat_v2(
        struct llama_context * ctx,
        llama_token_data_array * candidates,
        float   tau,
        float   eta,
        float * mu);

/// @details Selects the token with the highest probability.
///          Does not compute the token probabilities. Use llama_sample_softmax() instead.
LLAMA_API llama_token llama_sample_token_greedy(
        struct llama_context * ctx,
        llama_token_data_array * candidates);

/// @details Randomly selects a token from the candidates based on their probabilities.
LLAMA_API llama_token llama_sample_token(
        struct llama_context * ctx,
        llama_token_data_array * candidates);

/// @details Accepts the sampled token into the grammar
LLAMA_API void llama_grammar_accept_token(
        struct llama_context * ctx,
        struct llama_grammar * grammar,
        llama_token   token);

//
// Beam search
//

struct llama_beam_view {
    const llama_token * tokens;

    size_t n_tokens;
    float  p;        // Cumulative beam probability (renormalized relative to all beams)
    bool   eob;      // Callback should set this to true when a beam is at end-of-beam.
};

// Passed to beam_search_callback function.
// Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
// (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
// These pointers are valid only during the synchronous callback, so should not be saved.
struct llama_beams_state {
    struct llama_beam_view * beam_views;

    size_t n_beams;               // Number of elements in beam_views[].
    size_t common_prefix_length;  // Current max length of prefix tokens shared by all beams.
    bool   last_call;             // True iff this is the last callback invocation.
};

// Type of pointer to the beam_search_callback function.
// void* callback_data is any custom data passed to llama_beam_search, that is subsequently
// passed back to beam_search_callback. This avoids having to use global variables in the callback.
typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);

/// @details Deterministically returns entire sentence constructed by a beam search.
/// @param ctx Pointer to the llama_context.
/// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
/// @param callback_data A pointer that is simply passed back to callback.
/// @param n_beams Number of beams to use.
/// @param n_past Number of tokens already evaluated.
/// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
LLAMA_API void llama_beam_search(
        struct llama_context * ctx,
        llama_beam_search_callback_fn_t   callback,
        void * callback_data,
        size_t   n_beams,
        int   n_past,
        int   n_predict);

// Performance information
LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);

LLAMA_API void llama_print_timings(struct llama_context * ctx);
LLAMA_API void llama_reset_timings(struct llama_context * ctx);

// Print system information
LLAMA_API const char * llama_print_system_info(void);

// Set callback for all future logging events.
// If this is not called, or NULL is supplied, everything is output on stderr.
LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);

LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);

#ifdef __cplusplus
}
#endif

// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
#ifdef LLAMA_API_INTERNAL

#include <vector>
#include <string>

struct ggml_tensor;

const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
        struct llama_context * ctx
);

#endif // LLAMA_API_INTERNAL

LLAMA_API int save_llama_model_file(const char * filename, const char * fn_vocab_model, struct llama_model * model, const ModelMetadata &metadata);

#endif // LLAMA_H
